## In Detail F# is a functional programming language that allows you to write simple code for complex problems. Currently, it is most commonly used in the financial sector. Quantitative finance makes ...heavy use of mathematics to model various parts of finance in the real world. If you are interested in using F# for your day-to-day work or research in quantitative finance, this book is a must-have. This book will cover everything you need to know about using functional programming for quantitative finance. Using a functional programming language will enable you to concentrate more on the problem itself rather than implementation details. Tutorials and snippets are summarized into an automated trading system throughout the book. This book will introduce you to F#, using Visual Studio, and provide examples with functional programming and finance combined. The book also covers topics such as downloading, visualizing and calculating statistics from data. F# is a first class programming language for the financial domain. ## Approach The approach is to guide you as a reader from the basics of functional programming and F# to more complex tasks using tutorials and a lot of code examples. As you gain more confidence through out the book, you will be able to modify and write your own code to solve various problems in finance. ## Who this book is for If you are a practitioner of quantitative finance, economics, or mathematics and wish to learn F#, then this book is for you. You may have a basic conceptual understanding of financial concepts and models, but no previous knowledge is expected.
Ecological Models and Data in R is the first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers ...everything they need to know in order to use maximum likelihood, information-theoretic, and Bayesian techniques to analyze their own data using the programming language R. Drawing on extensive experience teaching these techniques to graduate students in ecology, Benjamin Bolker shows how to choose among and construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. The book also covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions. It requires no programming background--only basic calculus and statistics. Practical, beginner-friendly introduction to modern statistical techniques for ecology using the programming language R Step-by-step instructions for fitting models to messy, real-world data Balanced view of different statistical approaches Wide coverage of techniques--from simple (distribution fitting) to complex (state-space modeling) Techniques for data manipulation and graphical display Companion Web site with data and R code for all examples
This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkinson's Grammar of Graphics to create a powerful and flexible system for creating data ...graphics. It teaches how to create graphics in R using ggplot.
This book takes a single line of code--the extremely concise BASIC program for the Commodore 64 inscribed in the title--and uses it as a lens through which to consider the phenomenon of creative ...computing and the way computer programs exist in culture. The authors of this collaboratively written book treat code not as merely functional but as a text--in the case of 10 PRINT, a text that appeared in many different printed sources--that yields a story about its making, its purpose, its assumptions, and more. They consider randomness and regularity in computing and art, the maze in culture, the popular BASIC programming language, and the highly influential Commodore 64 computer.
This fast-moving tutorial introduces you to OCaml, an industrial-strength programming language designed for expressiveness, safety, and speed. Through the book's many examples, you'll quickly learn ...how OCaml stands out as a tool for writing fast, succinct, and readable systems code using functional programming. Real World OCaml takes you through the concepts of the language at a brisk pace, and then helps you explore the tools and techniques that make OCaml an effective and practical tool. You'll also delve deep into the details of the compiler toolchain and OCaml's simple and efficient runtime system. This second edition brings the book up to date with almost a decade of improvements in the OCaml language and ecosystem, with new chapters covering testing, GADTs, and platform tooling. This title is also available as open access on Cambridge Core, thanks to the support of Tarides. Their generous contribution will bring more people to OCaml.
The dynamic, student focused textbook provides step-by-step instruction in the use of R and of statistical language as a general research tool. It is ideal for anyone hoping to: Complete an ...introductory course in statistics Prepare for more advanced statistical courses Gain the transferable analytical skills needed to interpret research from across the social sciences Learn the technical skills needed to present data visually Acquire a basic competence in the use of R. The book provides readers with the conceptual foundation to use applied statistical methods in everyday research. Each statistical method is developed within the context of practical, real-world examples and is supported by carefully developed pedagogy and jargon-free definitions. Theory is introduced as an accessible and adaptable tool and is always contextualized within the pragmatic context of real research projects and definable research questions. Author Robert Stinerock has also created a wide range of online resources, including: R scripts, complete solutions for all exercises, data files for each chapter, video and screen casts, and interactive multiple-choice quizzes.
Pro Office 365 Development Collins, Mark; Enterprises, Creative; Mayberry, Michael
2012, 2012-02-17T00:00:00, 2012-02-24, c2012
eBook
Pro Office 365 Development is a practical, hands-on guide to building cloud-based solutions using the Office 365 platform. This groundbreaking offering from Microsoft provides enterprise-class ...collaborative solutions at an affordable price, and this book shows you how to use the Office 365 platform to easily build amazing custom applications, including coding for Excel Services, Microsoft Access, and SharePoint Online.This book provides everything you'll need to start developing custom solutions. You'll find step-by-step instructions for providing custom features using the cloud-based services, SharePoint Online, Exchange Online and Lync Online. There are lots of sample programs using Windows Presentation Foundation (WPF), JavaScript and Silverlight.Whether you want to build desktop client applications or browser-only solutions with Microsoft's new cloud-based productivity offering, this book will show you how to do it.Develop SharePoint solutions, including declarative workflowsUse Access and Excel services to quickly build SharePoint sitesBuild content-sensitive collaborative solutions with instant messaging and video conferencingWhat you'll learnConfigure and administer an enterprise Office 365 accountImplement declarative workflows using Visio and SharePoint DesignerCreate web databases using Access and SharePointUtilize the SharePoint object model in Visual StudioWrite Silverlight and JScript applications hosted in SharePointBuild WPF applications to expose Lync and Exchange servicesWho this book is forPro Office 365 Development is written for programmers who have experience with Visual Studio and .NET development and want to take the leap into cloud-based solutions. It is ideal for developers making their first foray into collaborative systems such as SharePoint and Exchange, as no prior experience is required.
A practical guide to learning visual perception for self-driving cars for computer vision and autonomous system engineers Key Features * Explore the building blocks of the visual perception system in ...self-driving cars * Identify objects and lanes to define the boundary of driving surfaces using open-source tools like OpenCV and Python * Improve the object detection and classification capabilities of systems with the help of neural networks Book Description The visual perception capabilities of a self-driving car are powered by computer vision. The work relating to self-driving cars can be broadly classified into three components - robotics, computer vision, and machine learning. This book provides existing computer vision engineers and developers with the unique opportunity to be associated with this booming field. You will learn about computer vision, deep learning, and depth perception applied to driverless cars. The book provides a structured and thorough introduction, as making a real self-driving car is a huge cross-functional effort. As you progress, you will cover relevant cases with working code, before going on to understand how to use OpenCV, TensorFlow and Keras to analyze video streaming from car cameras. Later, you will learn how to interpret and make the most of lidars (light detection and ranging) to identify obstacles and localize your position. You'll even be able to tackle core challenges in self-driving cars such as finding lanes, detecting pedestrian and crossing lights, performing semantic segmentation, and writing a PID controller. By the end of this book, you'll be equipped with the skills you need to write code for a self-driving car running in a driverless car simulator, and be able to tackle various challenges faced by autonomous car engineers. What you will learn * Understand how to perform camera calibration * Become well-versed with how lane detection works in self-driving cars using OpenCV * Explore behavioral cloning by self-driving in a video-game simulator * Get to grips with using lidars * Discover how to configure the controls for autonomous vehicles * Use object detection and semantic segmentation to locate lanes, cars, and pedestrians * Write a PID controller to control a self-driving car running in a simulator Who this book is for This book is for software engineers who are interested in learning about technologies that drive the autonomous car revolution. Although basic knowledge of computer vision and Python programming is required, prior knowledge of advanced deep learning and how to use sensors (lidar) is not needed.
For introductory-level Python programming and/or data-science courses. A ground-breaking, flexible approach to computer science and data science The Deitels' Introduction to Python for Computer ...Science and Data Science: Learning to Program with AI, Big Data and the Cloud offers a unique approach to teaching introductory Python programming, appropriate for both computer-science and data-science audiences. Providing the most current coverage of topics and applications, the book is paired with extensive traditional supplements as well as Jupyter Notebooks supplements. Real-world datasets and artificial-intelligence technologies allow students to work on projects making a difference in business, industry, government and academia. Hundreds of examples, exercises, projects (EEPs) and implementation case studies give students an engaging, challenging and entertaining introduction to Python programming and hands-on data science. The book's modular architecture enables instructors to conveniently adapt the text to a wide range of computer-science and data-science courses offered to audiences drawn from many majors. Computer-science instructors can integrate as much or as little data-science and artificial-intelligence topics as they'd like, and data-science instructors can integrate as much or as little Python as they'd like. The book aligns with the latest ACM/IEEE CS-and-related computing curriculum initiatives and with the Data Science Undergraduate Curriculum Proposal sponsored by the National Science Foundation.
